unsloth/Qwen-Image-GGUF

text-to-image model by unsloth

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🖥️ Demo&nbsp&nbsp | &nbsp&nbsp💬 WeChat (微信)&nbsp&nbsp | &nbsp&nbsp🫨 Discord&nbsp&nbsp We are thrilled to release Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. Experiments show strong general capabilities in both image generation and editing, with exceptional performance in text rendering, especially for Chinese. Install the latest version of diffusers The following contains a code snippet illustrating how to use the model to generate images based on text prompts: One of its standout capabilities is high-fidelity text rendering across diverse images. Whether it’s alphabetic languages like English or logographic scripts like Chinese, Qwen-Image preserves typographic details, layout coherence, and contextual harmony with stunning accuracy. Text isn’t just overlaid—it’s seamlessly integrated into the visual fabric. Beyond text, Qwen-Image excels at general image generation with support for a wide range of artistic styles. From photorealistic scenes to impressionist paintings, from…

unsloth/Qwen-Image-GGUF on Hugging Face A short preview, not the full document.

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Unsloth Dynamic 2.0

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Model overview

unsloth/Qwen-Image-GGUF is a text-to-image model repository published by unsloth on Hugging Face. The source reports the ggml library.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task: text-to-image
Library: ggml
Recent downloads (30 days): 51,677
Cumulative likes: 33
Hugging Face trending score: 0
Reported safetensors parameters: Not reported
Architecture: Not reported
License: apache-2.0
Access: Not gated by Hugging Face
Created: 2025-12-19T03:51:27.000Z
Last modified: 2025-12-22T08:23:42.000Z

Compatibility and lineage

Reported languages: en, zh

Reported base models: Qwen/Qwen-Image

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Model card and usage instructions

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